Ultrasonic diagnostic apparatuses have conventionally been widely used for diagnosing or observing blood flows in living organisms. An ultrasonic diagnostic apparatus generates and displays blood flow information using Doppler imaging, which visualizing ultrasonic reflection waves based on the Doppler shift. Examples of blood flow information generated and displayed by an ultrasonic diagnostic apparatus include color Doppler images and Doppler waveforms (Doppler spectrums).
A color Doppler image is an ultrasonic image achieved by color flow mapping (CFM). In CFM, an ultrasonic wave is transmitted and received a plurality of number of times along each of a plurality of scan lines. In CFM, an array of data acquired from the same position is then applied with moving target indicator (MTI) filtering so as to suppress the signals resulting from stationary or slow moving tissue structures (clutter signals) and to extract signals representing blood flows. In CFM, blood flow information such as velocity, variance, and power of blood flows are estimated based on the blood flow signals, and the resultant distribution of the estimation results is displayed as, for example, a two-dimensional color ultrasonic image (color Doppler image).
While a filter with a fixed coefficient, such as Butterworth infinite impulse response (IIR) filter or a polynomial regression filter, is usually used as an MTI filter, an adaptive MTI filter in which the coefficient is changed based on the input signal is also known.
An exemplary adaptive MTI filter calculates a tissue velocity from the signals before input to the MTI filter, and acquires signals having their phase difference cancelled out. The filter then selects one of MTI filter coefficients prepared in advance, based on the resultant signals. Another type of adaptive MTI filter generally referred to as an “eigenvector regression filter” is also known. To acquire a signal with its clutter component suppressed, this type of adaptive MTI filter calculates eigenvectors from a correlation matrix, and calculates the coefficient to be used in the MTI filter directly from the calculated eigenvectors. The approach is an application of the technique used in the principal component analysis, Karhunen-Loeve transform, or the eigenspace method. Also known as real-time implementation of the method that uses the “eigenvector regression filter” is a method using the “iterative power method” in calculating the eigenvectors.
The adaptive MTI filter described above calculates eigenvectors from a correlation matrix. The image quality of a video visualized with the adaptive MTI filter using eigenvectors, however, changes depending on what conditions the correlation matrix is calculated with. Therefore, the adaptive MTI filter using eigenvectors has not always resulted in a video with better image quality.